Last week Andrew Chen wrote an excellent post about the growth and potential decay of viral apps. Rather than just focusing on the elements of viral growth, Andrew also took into account the declining likelihood of an accepted invitation as you saturate a population, and the impact of churn. He provided a useful model to social media founders who are trying to estimate their growth, and what can go wrong when a viral app “jumps the shark”:

He notes:

* Early on, the growth of the curve is carried by the invitations
* However, over time the invitations start to slow down as you hit network saturation
* The retention coefficient affects your system by creating a “lagging indicator” on your acquisition – if you have good retention, even as your invites slow down, you won’t feel it as much
* If your retention sucks, then look out: The new invites can’t sustain the growth, and you end up with a rather dire “shark fin.”

I think this is a very useful model, but that it doesn’t quite predict what we typically see in real life. Rather than dropping to zero, failed viral apps typically hover at a steady level much lower than their peak. Since Andrew made the model available under “copyleft”, I made a small edit to his model. Rather than treating churn as a constant percentage of users in each time period, I treated it on a cohort level, with a higher churn rate in the early periods and lower churn as time goes on. This is similar to the churn profiles seen for subscriptions businesses such as AOL’s ISP business. (I was at AOL from 2002-2005 as SVP of Corporate Development, and then as GM of Netscape.) This model better matches active user graphs that we typically see for failed viral apps.

If you’re interested, the model is available for download here. Viral growth assumptions are in the yellow cells on the “viral acquisition” tab and churn assumptions and output are on the “user retention” tab.

http://www.scoreboard-media.com Brian Provost

I’m just a dumb search guy, but this is interesting. I tend to see more of the 2nd model on websites but we are now in an era where your average social networking app, no matter how cool it is or how hard you work on retention, could succumb to the disastrous platform it is married to.

For example, Flixster’s Movie app is great. But if it were reliant upon something like Friendster’s audience lifespan, it would be like the band playing as the Titanic sank.

Cross-platform development and audience hedging should rank right up there with product marketing’s retention initiatives for an application on the social layer.

J.T.

I sense… a great disturbance in the Force, as if thousands of financial analysts cried out in terror and were suddenly silenced…

http://www.xuru.com Jeremy Luebke

Very interesting. I’m going to try and apply something similar to a free-trial to paid membership type site and see how it compares.

http://vielmetti.typepad.com Edward Vielmetti

This curve looks eerily reminiscent of the price of Cisco stock from 1996 to 2005, or perhaps the current stock price of Google, or perhaps housing prices overall in the current subprime bubble.

Long initial steady “organic” growth at the natural rate, increasing “speculative” growth over time until you hit a tipping point where you get hypergrowth (or hyperinflation) and then the bubble bursts and you revert slowly and painfully to the level of organic growth you once had (if you do survive the whole thing).

During the hypergrowth bubble period it’s really hard to tell what is going on because the usual indicators of success all look good – it’s that one very difficult day when everyone gives up on you that’s the hard part in hindsight to recognize.

http://weareindia.blogspot.com preetam mukherjee

@ brian: “Cross-platform development and audience hedging should rank right up there with product marketing’s retention initiatives for an application on the social layer.”
– I’m not religious, but Amen.

@ jeremy: looks like i returned to your blog at the right time. super post.

this is something we’ve actually found to be true, over and over again, in our research.
so instead of worrying about how we would drive traffic, we started worrying about how we could keep audiences engaged.

The link “http://www.scribd.com/word/download/2249620?extension=xls” seems not to be working anymore. Any hint where to get that file?

DefconUSA

Unfortunately, the link fails. From the information provided above, it appears the model is similar to the Bass Model for new product diffusion models. Professor Frank M. Bass first published the model in 1963 while at Perdue University. Since then, more work is published by a number of authors.

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